Logic-based intelligence for batteryless sensors
Logic-based intelligence for batteryless sensors
复制标题
无电池传感器基于逻辑的智能
DOI：
10.1145/3508396.3512870
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
F. Kawsar
中科院分区：
文献类型：
--
作者：
A. Bakar;Tousif Rahman;A. Montanari;Jie Lei;R. Shafik;F. Kawsar
嵌入式机器学习的出现使智能从云到边缘和传感器的迁移。为了探索这些智能传感器的广泛部署的实用性，我们超越了基于传统的基于算术的神经网络（NNS），以基于逻辑的学习算法称为TSETLIN MANCHER（TM）。 TMS尚未通过通用微控制器实施和探索，尤其是间歇性动力。在本文中，我们认为它们的简单架构使它们成为无电Bowd ML系统的有前途的候选人。但是，以当前形式，由于训练有素的模型的大量内存足迹，它们不适合在资源受限的传感器上部署。为了解决这个问题，我们提出了一种基于跑步长度编码的无损压缩方案，并根据视力和声学工作负载进行评估。我们表明，我们的编码可以压缩多达99％的模型而不会准确损失。与原始的Tsetlin机器算法相比，这转化为较低的记忆足迹和更好的能源效率（最高4.9倍），并且与二进制神经网络相比，可提供有希望的贸易折扣。
The emergence of embedded machine learning has enabled the migration of intelligence from the cloud to the edge and to the sensors. To explore the practicalities of wide-spread deployments of these intelligent sensors, we look beyond traditional arithmetic-based neural networks (NNs) to the logic-based learning algorithm called the Tsetlin Machine (TM). TMs have not yet been implemented and explored on general purpose microcontrollers especially that are intermittently powered. In this paper, we argue that their simple architecture makes them a promising candidate for batteryless ML systems. However, in their current form, they are not suitable to be deployed on resource-constrained sensors because of the substantial memory footprint of trained models. To tackle this issue, we propose a lossless compression scheme based on run-length encoding and evaluate against standard TMs for vision and acoustic workloads. We show that our encoding can compress the model by up to 99% without accuracy loss. This translates into lower memory footprint and better energy efficiency (up to 4.9x) compared to the original Tsetlin Machine algorithm, and provides promising trade offs when compared against binary neural networks.
DOI：
10.1145/3297858.3304011
发表时间：
2018-09
期刊：
Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
影响因子：
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作者：
Graham Gobieski;Nathan Beckmann;Brandon Lucia
通讯作者：
Graham Gobieski;Nathan Beckmann;Brandon Lucia
DOI：
10.1145/3373376.3378476
发表时间：
2020-03
期刊：
Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems
影响因子：
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作者：
Vito Kortbeek;K. Yıldırım;A. Bakar;Jacob M. Sorber;Josiah D. Hester;P. Pawełczak
通讯作者：
Vito Kortbeek;K. Yıldırım;A. Bakar;Jacob M. Sorber;Josiah D. Hester;P. Pawełczak